Capital Efficiency in Multi-factor Portfolios [Flirting with Models]

The debate for the best way to build a multi-factor portfolio – mixed or integrated – rages on. Last week we explored whether the argument held that integrated portfolios are more capital efficient than mixed portfolios in realized return data for several multi-factor ETFs. This week we explore

Hidden Markov Models for Regime Detection using R [Quant Start]

In the previous article in the series Hidden Markov Models were introduced. They were discussed in the context of the broader class of Markov Models. They were motivated by the need for quantitative traders to have the ability to detect market regimes in order to adjust how their quant strategies

Cloud-Based Automated Trading System with Machine Learning [Quant Insti]

Maxime Fages Maxime’s career spanned across the strategic aspects of value and risk, with a particular focus on trading behaviors and market microstructure over the past few years. He embraced a quantitative angle in M&A, fund management or currently corporate strategy and has always been an

Probability of Black Swan Events at NYSE [Quant at Risk]

The prediction of extreme rare events (EREs) in the financial markets remains one of the toughest problems. Firstly because of a very limited knowledge we have on their distribution and underlying correlations across the markets. Literally, we walk in dark, hoping it won’t happen today, not to the

A Quant's Approach to Building Trading Strategies: Part One [Quandl]

Recently, Quandl interviewed a senior quantitative portfolio manager at a large hedge fund. We spoke about how she builds trading strategies–how she transitions from an abstract representation of the market to something concrete with genuine predictive powers. Can you tell us how you design new

Exploring mean reversion and cointegration: part 2 [Robot Wealth]

In the first post in this series, I explored mean reversion of individual financial time series using techniques such as the Augmented Dickey-Fuller test, the Hurst exponent and the Ornstein-Uhlenbeck equation for a mean reverting stochastic process. I also presented a simple linear mean reversion

pysystemtrade [Investment Idiocy]

There are already many python packages where you can back test trading strategies. Some of them also include a framework for automatic execution and complete position management. I can't give an exhaustive list but I'll pick out: - Quantopian's zipline - BT - pythalesians -

Exploring mean reversion and cointegration with Zorro and R: part 1 [Robot Wealth]

This series of posts is inspired by several chapters from Ernie Chan’s highly recommended book Algorithmic Trading. The book follows Ernie’s first contribution, Quantitative Trading, and focuses on testing and implementing a number of strategies that exploit measurable market inefficiencies.

Better Tests with Oversampling [Financial Hacker]

The more data you use for testing or training your strategy, the less bias will affect the test result and the more accurate will be the training. The problem: price data is always in short supply. Even shorter when you must put aside some part for out-of-sample tests. Extending the test or training

Unsupervised candlestick classification for fun and profit – part 2 [Robot Wealth]

In the last article, I described an application of the k-means clustering algorithm for classifying candlesticks based on the relative position of their open, high, low and close. This was a simple enough exercise, but now I tackle something more challenging: isolating information that is both

GMO Flows Turn Negative - An Ominous Sign for Risk Taking [EconomPic]

I have a ton of respect for the way in which GMO manages money (their guts to be massively contrarian if that is their view) and I think their thought leadership is about as good as it gets in the industry. That said, I have long had an issue in the way in which they think about investor behavior

Returns clustering with K-means algorithm [Quant Dare]

Do you know how a fireman and the direcion of a financial time series are related? If your answer is no, you’re reading the right post. Voronoi diagram Suppose that you are a worker in an emergency center in a city and your job is to tell the pilots of firefighter helicopters to take off. You

Video: James Simons - Numberphile [YouTube]

James Harris Simons has been described as "the world's smartest billionaire", amassing a fortune through the clever use of mathematics and computers. He is now a renowned philanthropist.

Fitness Landscape Analysis for Computational Finance [Turing Finance]

Some of the most interesting new research coming out of the Computational Intelligence Research Group (CIRG), which is applicable to numerous computational finance and machine learning optimization problems, is the development of fitness landscape analysis techniques. Fitness landscape analysis aims

Testing the Random Walk Hypothesis with R, Part One [Turing Finance]

Whilst working on some code for my Masters I kept thinking, "it would be really awesome if there was an R package which just consumed a price series and produced a data.frame of results from multiple randomness tests at multiple frequencies". So I decided to write one and it's named

R-view: Backtesting – Harvey & Liu (2015) [Open Source Quant]

In this post i take an R-view of “Backtesting – Harvey & Liu (2015).” The authors propose an alternative to the commonly practiced 50% discount that is applied to reported Sharpe ratios when evaluating backtests of trading strategies. The reason for the discount is due to the inevitable

Antonacci's Composite Dual Momentum [Allocate Smartly]

This is a test of Gary Antonacci’s “Composite Dual Momentum” strategy from his seminal paper: Risk Premia Harvesting Through Dual Momentum. The model uses Antonacci’s unique approach to measuring momentum, which considers both absolute (aka time-series) and relative (aka cross-sectional)

Mathematics and economics: A reality check [Mathematical Investor]

One of us (Marcos Lopez de Prado) has published the article Mathematics and economics: A reality check in the Journal of Portfolio Management. The article is open-access — there is no fee for viewing or downloading. Lopez de Prado argues that while economics is arguably one the most mathematical

A Framework for a Short VIX Allocation [EconomPic]

It has historically paid to be a seller of volatility for at least two reasons... 1) Volatility is typically overpriced relative to realized volatility The chart on the left shows the VIX index (predicted volatility) relative to the forward realized volatility of the S&P 500, while the chart on

Systematic risk management [Investment Idiocy]

As the casual reader of this blog (or my book) will be aware, I like to delegate my trading to systems, since humans aren't very good at it (well, I'm not). This is quite a popular thing to do; many systematic investment funds are out there competing for your money; from simple passive

Are Stocks Actually Undervalued? [Flirting with Models]

Summary We have noticed the market reaching a broad consensus that equities are overvalued, implying a drag on forward expected returns as valuation multiples contract. While there is often great wisdom in the crowd, there can also be great madness. We believe it is prudent to consider how the crowd

DIY Quants [Largecap Trader]

With the recent announcement at Point72 of a $250MM investment in quantitative trading platform Quantopian and a recent FT article, there has been a surge in interest in Do-It-Yourself quant strategies. Here’s one from WSJ. There are some significant challenges for these startups in my opinion*:

Low Volatility in Not Low Risk [Flirting with Models]

Summary Low volatility equity ETFs have seen huge inflows this year, driven by both a compelling story (risk-managed equity investing) and significant outperformance. Critics have raised concerns that short-term performance chasers have driven up the valuations of these strategies, increasing the

What if Factors Rarely Matter? [EconomPic]

Back in December I wrote that It's Generally Smart to Avoid Credit Risk outlining that more than 100% of credit's excess performance over time has come when the level of credit spread was extreme. What if the same were true for well known investment factors? Taking a Look at the Small Cap

Optimization Mean Reversion [Alvarez Quant Trading]

Often one runs a optimization of a testing idea, then using some set metrics from these results, one picks a variation to trade. What often comes as a surprise to people, and myself the first time I saw this, is that your optimization runs are often mean reverting. What do I mean by this? For

Deep Learning with Theano - Part 1: Logistic Regression [Quant Start]

Over the last ten years the subject of deep learning has been one of the most discussed fields in machine learning and artificial intelligence. It has produced state-of-the-art results in areas as diverse as computer vision, image recognition, natural language processing and speech recognition.

The Changing Generations of Financial Data [Quandl]

As quants, we’re all aware that every model has a shelf-life. Sooner or later, the ideas and techniques behind every “proprietary” analytical technique diffuse into the broader world, at which point that technique is no longer the source of a competitive edge or alpha. What’s less well

FX: multivariate stochastic volatility - part 2 [Predictive Alpha]

In part 2 our mean-variance optimal FX portfolio is allowed to choose from multiple models each week based on a measure of goodness (MSSE). The risk-adjusted return improves as a result with the annualized Sharpe Ratio rising to 0.86 from 0.49. In part 1 we estimated a sequential multivariate

How to Learn Advanced Mathematics Without Heading to University - Part 1 [Quant Start]

I am often asked in emails how to go about learning the necessary mathematics for getting a job in quantitative finance or data science if it isn't possible to head to university. This article is a response to such emails. I want to discuss how you can become a mathematical autodidact using

If you're going to sin, sin systematically [Flirting with Models]

There is no holy grail investment style that will out-perform in all market environments. Being systematic and disciplined in our use of active strategies is the best way to capture out-performance because we don’t know when the out-performance will happen. Diversifying across several active

Dear Brokers... [Financial Hacker]

Whatever software we’re using for automated trading: We all need some broker connection for the algorithm to receive price quotes and place trades. Seemingly a simple task. And almost any broker supports it through a protocol such as FIX or REST, through an automated platform such as MT4™, or

Searching for an Efficient Market Regime Filter [Helix Trader]

The probability of our long term success as traders increases when we trade with the prevailing market trend. This means when trading stocks we should be buying when the overall market is rising and / or shorting when the overall market is falling. In order to filter trading opportunities therefore,

High Frequency Market Microstructure: Part 1 (Microstructure Noise) [Portfolio Effect]

Microstructure noise describes price deviation from its fundamental value induced by certain features of the market under consideration. Common sources of microstructure noise are: bid-ask bounce effect order arrival latency asymmetry of information discreteness of price changes Noise makes high

Getting Started: Building a Fully Automated Trading System [Quants Portal]

For the last 6 months I have been focused on the process of building the full technology stack of an automated trading system. I have come across many challenges and learnt a great deal about the two different methods of backtesting (Vectorised and Event driven). In my journey to building an event

VIX Trading Strategies in August [Volatility Made Simple]

We’ve tested 24 simple strategies for trading VIX ETPs on this blog (separate and unrelated to our own strategy). And while I can’t speak for all traders, based on all of my readings both academic and in the blogosphere, the strategies we’ve tested are broadly representative of how the vast

Bring Data [Dual Momentum]

When doing financial modeling, one of the first things to look at is if your empirical work makes sense. In other words, are there valid economic reasons why a model should work? This can help you avoid drawing erroneous conclusions based on creative data mining.[1] Next, you should look for

Dual Momentum for non-US Investors [Dual Momentum]

Gogi Grewal is an engineer and astute financial analyst who has been following my work for a number of years. He has an excellent grasp of dual momentum. Since Gogi lives in Canada, he decided to research the best way for non-US investors to utilize dual momentum. Gogi has generously offered to

A Basic Logical Invest Global Market Rotation Strategy [QuantStrat TradeR]

This may be one of the simplest strategies I've ever presented on this blog, but nevertheless, it works, for some definition of "works". Here's the strategy: take five global market ETFs (MDY, ILF, FEZ, EEM, and EPP), along with a treasury ETF (TLT), and every month, fully invest

Aluminum Smelting Cointegration Strategy in QSTrader [Quant Start]

In previous articles the concept of cointegration was considered. It was shown how cointegrated pairs of equities or ETFs could lead to profitable mean-reverting trading opportunities. Two specific tests were outlined–the Cointegrated Augmented Dickey-Fuller (CADF) test and the Johansen

A Very Different Kind of Trend Model [Following the Trend]

Trend following is all about following the price. Typically the only input we need for a trend following model is the price. But what if I told that we could make a kind of trend following model which does not use the price direction as an input at all? It also has no stops and no targets. In this

Kalman Filter-Based Pairs Trading Strategy In QSTrader [Quant Start]

Previously on QuantStart we have considered the mathematical underpinnings of State Space Models and Kalman Filters, as well as the application of the pykalman library to a pair of ETFs to dynamically adjust a hedge ratio as a basis for a mean reverting trading strategy. In this article we will

Podcast: This quants’ approach to designing algo strategies - Michael Halls-Moore [Chat With Traders]

For this episode I’m joined by Michael Halls-Moore, who runs QuantStart.com—a site well-known by many algorithmic traders. Prior to trading, Michael studied computational fluid dynamics and was the co-founder of a tech startup, before getting involved a small equity fund as a quant

David Varadi's Percentile Channels [Allocate Smartly]

This is a test of a tactical asset allocation strategy from David Varadi of CSS Analytics. I’ve been a long-time fan of David’s work. David always devises unique approaches to trading, swimming just outside the mainstream. This strategy is a good example of that. The strategy is notable for its

Finding Alpha pdf [Falkenblog]

My book The Missing Risk Premium is a steal at only $15, but my first book, Finding Alpha, is a $65, which is a bit much for anyone not expensing their books. Finding Alpha goes over why the current asset pricing model fails, with lots of evidence, explains why economists still like it, and then in

Capital correction (pysystemtrade) [Investment Idiocy]

This post is about how should you adjust the trading capital you have at risk given the profitability (or not) of your trading account. I'm posting this for three reasons. Firstly it's a pretty important topic. I address, in some detail, how to set your risk target for a given amount of

Quantopian Paper About In vs Out-of-Sample Performance of Trading Algorithms [Quantpedia]

When automated trading strategies are developed and evaluated using backtests on historical pricing data, there exists a tendency to overfit to the past. Using a unique dataset of 888 algorithmic trading strategies developed and backtested on the Quantopian platform with at least 6 months of

Are R^2s Useful In Finance? [QuantStrat TradeR]

This post will shed light on the values of R^2s behind two rather simplistic strategies - the simple 10 month SMA, and its relative, the 10 month momentum (which is simply a difference of SMAs, as Alpha Architect showed in their book DIY Financial Advisor. Not too long ago, a friend of mine named

Are 3-year track records meaningful? [Flirting with Models]

Many asset management decisions are based on the three-year track record. Three-years is suspiciously close to a common rule-of-thumb for calculating statistics, but in this case, it is a misapplication. With many strategies, short-term luck swamps long-term skill. Combining strategies can reduce

New Book from GestaltU: Adaptive Asset Allocation: Dynamic Global Portfolios to Profit in Good Times - and Bad [Amazon]

Build an agile, responsive portfolio with a new approach to global asset allocation Adaptive Asset Allocation is a no-nonsense how-to guide for dynamic portfolio management. Written by the team behind Gestaltu.com, this book walks you through a uniquely objective and unbiased investment philosophy

Volatility Futures and S&P500 Performance [Blue Sky AM]

Do Volatility Futures Provide Useful Information for Future S&P500 Performance? Volatility or VIX Futures are based on the S&P500 index and are calculated from the implied volatility of different option strike prices across different expiration periods. In contrast to the VIX index, VIX